Nudging the Aggregative Behavior of Noncooperative Agents

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We consider the problem of steering the aggregative behavior of a set of noncooperative price-taking agents to a desired point. Different from prevalent pricing schemes where the price is available for design, we resort to suitable nudge mechanisms to influence the behavior of the agents. In particular, a regulator sends a price prediction signal to the agents, based on which the agents decide on their actions. This prediction is potentially different from the actual price, which brings the issue of reliability. We take this into account by associating trust variables to the agents, implying that the agents do not blindly follow the prediction signal. These trust variables are updated depending on the history of the discrepancy between the actual and the predicted price. We carefully examine the resulting multi-components model and analyse its convergence properties. We show that under the proposed nudge mechanisms, the regulator gains agents' trust fully, and the aggregative behavior provably converges to a desired set point. The effectiveness of the approach is demonstrated by numerical examples.

Original languageEnglish
Title of host publication2020 59th IEEE Conference on Decision and Control, CDC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781728174471
Publication statusPublished - 14-Dec-2020
Event59th IEEE Conference on Decision and Control, CDC 2020 - Virtual, Jeju Island, Korea, Republic of
Duration: 14-Dec-202018-Dec-2020

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546


Conference59th IEEE Conference on Decision and Control, CDC 2020
Country/TerritoryKorea, Republic of
CityVirtual, Jeju Island

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